Tini1.5-8B-A1B

👁 Tini1.5-8B-A1B Logo

Tini1.5-8B-A1B is a production-grade fine-tuned version of the hybrid model architecture LiquidAI/LFM2.5-8B-A1B. This model is highly optimized for English Agentic Reasoning, seamlessly combining deep multi-turn Chain-of-Thought (CoT) and strict policy-compliant native system function calling capabilities.

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📊 Dataset Mixture

The model was Supervised Fine-Tuned (SFT) on a symmetric data matrix balancing deep reasoning tracks and advanced tool execution trajectories:

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🛠️ Training Techniques

To preserve the model's core architecture while focusing gradient updates entirely on structured agent workflows, the following configurations were applied: Train on Response Only

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🏃‍♂️ Quick Start & Inference Parameters Guide

💡 Recommended Decoding Parameters

• Agentic & Function-Calling Tasks: temperature: 0.1 | top_p: 0.95 | top_k: 50 | repetition_penalty: 1.00

• General Coding & Contextual Reasoning Tasks: temperature: 0.35 | top_p: 0.90 | top_k: 40 | repetition_penalty: 1.05

🚀 Python Example Script

import torch
from unsloth import FastLanguageModel
from transformers import TextStreamer

MODEL_PATH = "iselabvn/Tini1.5-8B-A1B"

# 1. Load model with 4-bit quantization and essential regex patches
model, tokenizer = FastLanguageModel.from_pretrained(
 model_name = MODEL_PATH,
 max_seq_length = 8192,
 dtype = torch.bfloat16,
 load_in_4bit = True,
 trust_remote_code = True,
 fix_mistral_regex = True
)
FastLanguageModel.for_inference(model)

# 2. Configure system prompt aligned with strict v1.5 validation guardrails
messages = [
 {
 "role": "system", 
 "content": "You are an advanced, high-efficiency executive Agent. If a tool requires a parameter that is missing from the prompt, DO NOT analyze or debate the schema. Stop thinking immediately and output a clear question asking the user for that parameter."
 },
 {
 "role": "user", 
 "content": "What is the current stock price of Nvidia (NVDA) today?"
 }
]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
text_streamer = TextStreamer(tokenizer, skip_prompt=True)

# 3. Generate structured output within a safe tokens boundary
with torch.no_grad():
 _ = model.generate(
 input_ids = inputs,
 streamer = text_streamer,
 max_new_tokens = 2048,
 use_cache = True,
 temperature = 0.1,
 top_p = 0.95,
 top_k = 50,
 repetition_penalty = 1.00
 )

Base model

LiquidAI/LFM2.5-8B-A1B-Base

LiquidAI/LFM2.5-8B-A1B

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